prediction.py

import streamlit as st 
import pandas as pd 
import numpy as np 
import pickle
import json
import sklearn

def run():
    with st.form('form-fifa_2022'):
        #field nama
        name = st.text_input('Name', value='')

        #field umur
        age = st.number_input('Age', min_value=16, max_value=60, value = 25, step=1, help='Usia pemain')

        #field tinggi badan
        height = st.slider('Height', 100, 250, 170)

        #field weight
        weight = st.number_input('Weight', 50, 150, 70)

        #field price
        price = st.number_input('Price', value=0)

        st.markdown('-----')

        #field attacking work rate
        attacking_work_rate = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index=1)

        #field defensive work rate
        defensive_work_rate = st.selectbox('Defensive Work Rate', ('Low', 'Medium', 'High'), index=1)

        #field pace total
        pace_total = st.number_input('Pace', min_value=0, max_value=100, value=50)
        #field shooting total
        shooting_total = st.number_input('Shooting', min_value=0, max_value=100, value=50)
        #filed passing total
        passing_total = st.number_input('Passing', min_value=0, max_value=100, value=50)
        #field dribbling total 
        dribbling_total = st.number_input('Dribbling', min_value=0, max_value=100, value=50)
        #filed defending total
        defending_total = st.number_input('Defending', min_value=0, max_value=100, value=50)
        #field physicality
        physicality = st.number_input('Physicality', min_value=0, max_value=100, value=50)

        #bikin submit button
        submitted = st.form_submit_button('Predict')


    #inference
    #load all files

    with open('list_cat_cols.txt', 'r') as file_1:
        list_cat_cols = json.load(file_1)
    with open('list_num_cols.txt', 'r') as file_2:
        list_num_cols = json.load(file_2)
    with open('model_scaler.pkl', 'rb') as file_3:
        model_scaler = pickle.load(file_3)
    with open('model_encoder.pkl', 'rb') as file_4:
        model_encoder = pickle.load(file_4)
    with open('model_lin_reg.pkl', 'rb') as file_5:
        model_lin_reg = pickle.load(file_5)
    data_inf = {
        'Name' : name,
        'Age' : age,
        'Height' : height,
        'Weight' : weight,
        'Price' : price,
        'AttackingWorkRate' : attacking_work_rate,
        'DefensiveWorkRate' : defensive_work_rate,
        'PaceTotal' :pace_total,
        'ShootingTotal': shooting_total,
        'PassingTotal' : passing_total,
        'DribblingTotal': dribbling_total,
        'DefendingTotal' :defending_total,
        'PhysicalityTotal':physicality,
    }


    data_inf = pd.DataFrame([data_inf])
    st.dataframe(data_inf)

    #logic ketika predic button ditekan

    if submitted:
    #split between numerical and categorical collumn
        data_inf_num = data_inf[list_num_cols]
        data_inf_cat = data_inf[list_cat_cols]


        #scalling dan encoding
        data_inf_num_scaled = model_scaler.transform(data_inf_num)
        data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
        data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis = 1)

        #preedict using linear reg model
        y_pred_inf = model_lin_reg.predict(data_inf_final)

        st.write('## Rating :', str(int(y_pred_inf)))

if __name__ == '__main__':
    run()